Our goal here is to look at the large and small particle size data.
Tempted to focus on station 043 again, so I can have fewer points and save my color coding.
library(tidyverse)
library(cowplot)
library(plotly)
pass <- function(x){x}
Load all data
unbinned_DepthSummary <- read_csv("dataOut/unbinned_DepthSummary.csv")
unbinned_EachSize <- read_csv("dataOut/unbinned_EachSize.csv")
binned_DepthSummary <- read_csv("dataOut/binned_DepthSummary.csv")
binned_EachSize <- read_csv("dataOut/binned_EachSize.csv")
Binned
binned_class_DepthSummary <- binned_DepthSummary %>%
select(depth, profile, tot_TotParticles:big_speed) %>%
pivot_longer(cols = c(-depth, -profile)) %>%
separate(name, c("class", "variable"), extra = "merge") %>%
pivot_wider(names_from = variable)
binned_class_DepthSummary %>% head
binndPlotable <- binned_class_DepthSummary %>% filter(profile == "stn_043", class != "tot")
plot_nparticles <- binndPlotable %>% ggplot(aes(x = nparticles, y = depth, shape = class)) + geom_point(alpha = 0.5, size = 3) + scale_y_reverse(breaks = seq(from = 0, to = 2500, by = 100)) + scale_x_log10() + guides(colour = FALSE) + labs(x = "#Particles/L") + theme_cowplot()
ggplotly(plot_nparticles)
# ggplotly(plot_biovolume)
# ggplotly(plot_flux)
# ggplotly(plot_speed)
# ggplotly(plot_psd)
#
# cowplot::plot_grid(plot_nparticles,plot_biovolume, plot_flux, plot_speed, plot_psd)
long_binned <- binned_class_DepthSummary <- binned_DepthSummary %>%
select(depth, profile, tot_TotParticles:big_speed) %>%
pivot_longer(cols = c(-depth, -profile)) %>%
separate(name, c("class", "variable"), extra = "merge") %>%
filter(class != "tot", profile == "stn_043", !variable %in% c("nnparticles", "flux", "TotParticles"))
head(long_binned)
longPlt <- long_binned %>% ggplot(aes(x = value, y = depth, shape = class, fill = class)) + geom_point(alpha = 0.5, size = 3) + scale_y_reverse(breaks = seq(from = 0, to = 2500, by = 200)) + scale_x_log10() + guides(colour = FALSE) + labs(x = "Value") + theme_cowplot() + facet_wrap(~variable, scales = "free_x", ncol = 2) + scale_shape_manual(values = c(21, 22)) + scale_fill_manual(values = c("small" = "red", "big" = "blue"))
longPlt

We would have to plot these seperately to have appropriate x labels.
Fun fact. The bulk of flux is from small particles.
PSD
Huh. There is a general flattening of the curve. That is, the large particles generally have a steeper PSD than the smaller particles.
long_psd_binned <- binned_DepthSummary %>%
select(depth, profile, small_icp:big_psd, psd, icp) %>%
pivot_longer(cols = c(-depth, -profile)) %>%
separate(name, c("class", "variable"), extra = "merge", fill = "left") %>%
mutate(class = replace_na(class, "tot")) %>%
filter(profile == "stn_043") %>%
#pivot_wider(names_from = variable) %>%
pass
longPsdPlt <- long_psd_binned %>% ggplot(aes(x = value, y = depth, shape = class, fill = class)) + geom_point(alpha = 0.5, size = 3) + scale_y_reverse(breaks = seq(from = 0, to = 2500, by = 200)) + scale_x_continuous() + guides(colour = FALSE) + labs(x = "Value") + theme_cowplot() + facet_wrap(~variable, scales = "free_x", ncol = 2) + scale_shape_manual(values = 21:25) +
scale_fill_manual(values = c("tot" = "black", "big" = "blue", "small" = "red"))
longPsdPlt

- scale_fill_manual(values = c(“black”, “blue”, “red”), breaks = c(“tot”, “big”, “small”))
Something is screwing up my legend, but the fact is the small particles define the PSD. Big particles tend to have steeper PSD, indicating a flattening of the curve at smaller values.
https://stackoverflow.com/questions/21865207/adding-legend-ggplot-doesnt-work
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c21hbGxlciB2YWx1ZXMuCgpodHRwczovL3N0YWNrb3ZlcmZsb3cuY29tL3F1ZXN0aW9ucy8yMTg2NTIwNy9hZGRpbmctbGVnZW5kLWdncGxvdC1kb2VzbnQtd29yawoK